This specification relates to efficiently training and using computer-implemented classifiers.
Support Vector Machines (SVMs) are tools for data classification. Non-linear SVMs map training and testing instances to a high dimensional space by a nonlinear function. While nonlinear SVMs have good accuracy in classification, nonlinear SVMs also require large amounts of memory. In general, a technique known as “the kernel trick” is used to reduce the memory demands of a nonlinear SVM However, training nonlinear SVMs, even with the kernel trick, still requires a great deal of memory and a great deal of training time, especially for large data sets.
Decomposition methods are a way to train nonlinear SVMs with less memory consumption than standard techniques. However, decomposition methods still require considerable training time for training data with a large number of features. In addition, the classifying procedure is slow.